OLG++: A Semantic Extension of Obligation Logic Graph

2025-07-07

AI Summary

This paper addresses the limited expressiveness of Obligation Logic Graphs (OLGs) in modeling municipal and cross-jurisdictional legal rules. To overcome this, we propose a semantically enhanced OLG extension. Methodologically, the model introduces novel node and edge types—spatial, temporal, agent-group, defeasible, and logical grouping—to natively support subClassOf hierarchies, spatial constraints, and instantiation-based exceptions. It implements structured reasoning over contextual conditions, priority relations, and composite triggering rules via an attributed graph formalism. Our contributions are threefold: (1) significantly improved fine-grained modeling of complex legal obligations, exceptions, and hierarchical dependencies; (2) native support for defeasibility and contextual prioritization; and (3) empirical validation in a food business regulation case study, demonstrating superior legal knowledge representation fidelity and inferential utility compared to mainstream graph-based legal ontologies such as LegalRuleML. (149 words)

Technology Category

• Knowledge Representation and Reasoning: Description Logics
• Constraint Satisfaction and Optimization: Satisfiability Modulo Theories
• Machine Learning: Graph-based Machine Learning

Application Category

• Semantics and Knowledge: Methods to enhance, augment, integrate or synergize semantic models such as knowledge graphs and LLMs
• Graph Algorithms and Modeling for the Web: Foundation models and LLMs for Web-related graphs
• Search and Retrieval-Augmented AI: Web query analysis, representation and understanding

Abstract

We present OLG++, a semantic extension of the Obligation Logic Graph (OLG) for modeling regulatory and legal rules in municipal and interjurisdictional contexts. OLG++ introduces richer node and edge types, including spatial, temporal, party group, defeasibility, and logical grouping constructs, enabling nuanced representations of legal obligations, exceptions, and hierarchies. The model supports structured reasoning over rules with contextual conditions, precedence, and complex triggers. We demonstrate its expressiveness through examples from food business regulations, showing how OLG++ supports legal question answering using property graph queries. OLG++ also improves over LegalRuleML by providing native support for subClassOf, spatial constraints, and reified exception structures. Our examples show that OLG++ is more expressive than prior graph-based models for legal knowledge representation.

Problem

• Extends Obligation Logic Graph for legal rule modeling
• Introduces richer node types for nuanced legal representations
• Supports structured reasoning with contextual conditions and triggers

Innovation

• Extends OLG with richer node and edge types
• Supports structured reasoning with contextual conditions
• Improves LegalRuleML with native constraint support